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A Novelty Automatic Fingerprint Matching System

  • Tianding Chen
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)

Abstract

In recently years, fingerprint identification becomes more and more important for security. In this paper, it introduces an automatic fingerprint matching system(AFMS). It includes three stages: fingerprint classification, minutiae extraction and fingerprint matching. In the stage of fingerprint classification, a fingerprint is classified into one of the five types, Arch, Right Loop, Left Loop, Whorl and Others. In the stage of minutiae extraction, the minutiae, composed of ridge endings and bifurcations, are detected. In the stage of fingerprint matching, a matching score between two minutiae pattern is computed. Our AFMS is tested on 6 databases of fingerprint images. According to the type of top three matches, the recognition rates are excellent. The results reveal the expected performance and applicability of the system. They prove as well the availability of design methodology proposed in this system.

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References

  1. 1.
    Jea, T. Y., Govindaraju, V.: A Minutiae-based Partial Fingerprint Recognition System, Pattern Recognition, 38(10), (2005)1672–1684CrossRefGoogle Scholar
  2. 2.
    Naccache, N.J., Shinghal, P.: An Investigation into the Skeletonization Approach of Hilditch, Pattern Recognition, 17(3), (1984)279–284CrossRefGoogle Scholar
  3. 3.
    Karu, K., Jain, A.K.: Fingerprint Classification, Pattern Recognition, 29(3), (1996)284–404CrossRefGoogle Scholar
  4. 4.
    Ko, T.: Fingerprint Enhancement by Spectral Analysis Techniques, The Proceedings of Applies Imagery Pattern Recognition Workshop, (2002) 133–139Google Scholar
  5. 5.
    Bazen, A.M., Gerez, S.H.: Directional Field Computation for Fingerprints Based on the Principal Component Analysis of Local Gradients, In Proc. ProRISC2000 Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, Nov. (2000)Google Scholar
  6. 6.
    Bazen, A.M., Gerez, S.H.: Segmentation of Fingerprint Images, In Proc. ProRISC2001 Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, Nov. (2001)Google Scholar
  7. 7.
    A. Senior: A Combination Fingerprint Classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), (2001)1165–1174CrossRefGoogle Scholar
  8. 8.
    Conti, V., Pilato, G., Vitabile, S., Sorbello, F.: Verification of Ink-on-paper Fingerprints by Using Image Processing Techniques and a New Matching Operator, VIII Convegno AI*IA, Siena 10–13, (2002)594–601Google Scholar
  9. 9.
    Espinosa-Duro, V.: Minutiae Detection Algorithm for Fingerprint Recognition, IEEE Aerospace and Electronics Systems Magazine, 17(3), (2002)7–10CrossRefGoogle Scholar
  10. 10.
    Maio, D., maltoni, D., Cappelli, R., Wayman, J.L, Jain, A.K.: FVC2000: Fingerprint Verification Competition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3), (2002)811–816CrossRefGoogle Scholar
  11. 11.
    Zorita, D. S., Garcia, J.O., Lianas, S. C., Rodriguez, J. G.: Minutiae Extraction Scheme for Fingerprint Recognition Systems, International Conference on Image Processing, Vol. 2, (2001)254–257Google Scholar
  12. 12.
    Zhang, Q.Z., Yan, H.: Fingerprint Classification Based on Extraction and Analysis of Singularities and Pseudo Ridges, Pattern Recognition, 37(11), (2004)2233–2243CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tianding Chen
    • 1
  1. 1.Institute of Communications and Information TechnologyZhejiang Gongshang UniversityHangzhouChina

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